让大模型记忆更懂用户偏好，动态更新长期记忆。
Preference-Aware Memory Update for Long-Term LLM Agents
- 用滑动平均与指数加权平均融合，动态捕捉用户偏好变化。
- 在五个任务上提升基线模型输出质量，验证长期对话效果。
- 适合需要持续学习用户习惯的智能助手、个性化服务场景。
大型语言模型代理的推理能力关键在于其长时记忆的利用能力。整合长时记忆机制可使代理基于历史交互做出更明智决策。尽管近期研究显著提升了记忆存储与检索性能，如通过密集向量编码实现相似性搜索或构建结构化知识图谱，但现有方法在记忆更新方面仍存在不足，尤其缺乏根据用户行为和上下文演变动态优化偏好记忆表征的机制。为此，我们提出偏好感知的记忆更新机制（PAMU），实现动态且个性化的记忆精炼。PAMU结合滑动窗口均值（SW）与指数移动平均（EMA），构建融合的偏好感知表示，同时捕捉短期波动与长期用户倾向。我们在LoCoMo数据集的五个任务场景中进行实验，结果表明该机制可显著提升五种基线模型的输出质量，验证了其在长期对话中的有效性。
原文摘要 · Abstract (English)
One of the key factors influencing the reasoning capabilities of LLM-based agents is their ability to leverage long-term memory. Integrating long-term memory mechanisms allows agents to make informed decisions grounded in historical interactions. While recent advances have significantly improved the storage and retrieval components, by encoding memory into dense vectors for similarity search or organizing memory as structured knowledge graphs most existing approaches fall short in memory updating. In particular, they lack mechanisms for dynamically refining preference memory representations in response to evolving user behaviors and contexts. To address this gap, we propose a Preference-Aware Memory Update Mechanism (PAMU) that enables dynamic and personalized memory refinement. By integrating sliding window averages (SW) with exponential moving averages (EMA), PAMU constructs a fused preference-aware representation that captures both short-term fluctuations and long-term user tendencies. We conduct experiments on five task scenarios of the LoCoMo dataset, and the results show that our mechanism can significantly improve the output quality of LLM in five baselines, validating its effectiveness in long-term conversations.
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